Precise Tumor Research Articles

An Oxidative Stress Nano-Amplifier for Improved Tumor Elimination and Combined Immunotherapy.

Amplifying oxidative stress to disrupt intracellular redox homeostasis can accelerate tumor cell death. In this work, an oxidative stress amplifier (PP@T) is prepared for enhanced tumor oxidation therapy to reduce tumor growth and metastases. The nano-amplifier has been successfully constructed by embedding MTH1 inhibitor (TH588) in the PDA-coated porphyrin metal-organic framework PCN-224. The controllable-released TH588 is demonstrated from pores can hinder MTH1-mediated damage-repairing process by preventing the hydrolysis of 8-oxo-dG, thereby amplifying oxidative stress and exacerbating the oxidative DNA damage induced by the sonodynamic therapy of PP@T under ultrasound irradiation. Furthermore, PP@T can effectively induce immunogenic cell death to trigger systemic anti-tumor immune response. When administered in combination with immune checkpoint blockade, PP@T not only impedes the progression of the primary tumor but also achieves obvious antimetastasis in breast cancer murine models, including orthotopic and artificial whole-body metastasis models. Furthermore, the nanoplatform also provides photoacoustic imaging for in vivo treatment guidance. In conclusion, by amplifying oxidative stress and reactive oxygen species sensitized immunotherapy, this image-guided nanosystem shows potential for highly specific, effective combined therapy against tumor cells with negligible side-effects to normal cells which will provide a new insight for precise tumor treatment.

Combining Radiomics and Autoencoders to Distinguish Benign and Malignant Breast Tumors on US Images.

Background US is clinically established for breast imaging, but its diagnostic performance depends on operator experience. Computer-assisted (real-time) image analysis may help in overcoming this limitation. Purpose To develop precise real-time-capable US-based breast tumor categorization by combining classic radiomics and autoencoder-based features from automatically localized lesions. Materials and Methods A total of 1619 B-mode US images of breast tumors were retrospectively analyzed between April 2018 and January 2024. nnU-Net was trained for lesion segmentation. Features were extracted from tumor segments, bounding boxes, and whole images using either classic radiomics, autoencoder, or both. Feature selection was performed to generate radiomics signatures, which were used to train machine learning algorithms for tumor categorization. Models were evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity and were statistically compared with histopathologically or follow-up-confirmed diagnosis. Results The model was developed on 1191 (mean age, 61 years ± 14 [SD]) female patients and externally validated on 50 (mean age, 55 years ± 15]). The development data set was divided into two parts: testing and training lesion segmentation (419 and 179 examinations) and lesion categorization (503 and 90 examinations). nnU-Net demonstrated precision and reproducibility in lesion segmentation in test set of data set 1 (median Dice score [DS]: 0.90 [IQR, 0.84-0.93]; P = .01) and data set 2 (median DS: 0.89 [IQR, 0.80-0.92]; P = .001). The best model, trained with 23 mixed features from tumor bounding boxes, achieved an AUC of 0.90 (95% CI: 0.83, 0.97), sensitivity of 81% (46 of 57; 95% CI: 70, 91), and specificity of 87% (39 of 45; 95% CI: 77, 87). No evidence of difference was found between model and human readers (AUC = 0.90 [95% CI: 0.83, 0.97] vs 0.83 [95% CI: 0.76, 0.90]; P = .55 and 0.90 vs 0.82 [95% CI: 0.75, 0.90]; P = .45) in tumor classification or between model and histopathologically or follow-up-confirmed diagnosis (AUC = 0.90 [95% CI: 0.83, 0.97] vs 1.00 [95% CI: 1.00,1.00]; P = .10). Conclusion Precise real-time US-based breast tumor categorization was developed by mixing classic radiomics and autoencoder-based features from tumor bounding boxes. ClinicalTrials.gov identifier: NCT04976257 Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Bahl in this issue.

Programmable Bacteria with Dynamic Virulence Modulation System for Precision Antitumor Immunity.

Engineered bacteria-mediated antitumor approaches have been proposed as promising immunotherapies for cancer. However, the off-target bacterial toxicity narrows the therapeutic window. Living microbes will benefit from their controllable immunogenicity within tumors for safer antitumor applications. In this study, a genetically encoded microbial activation strategy is reported that uses tunable and dynamic expression of surface extracellular polysaccharides to improve bacterial biocompatibility while retaining therapeutic efficacy. Based on screening of genes associated with Salmonella survival in macrophages, a novel attenuated Salmonella chassis strain AIS (htrA gene-deficient) highly enriched in tumors after administration and rapidly cleared from normal organs are reported. Subsequently, an engineered bacterial strain, AISI-H, is constructed based on the AIS strain and an optimized quorum-sensing regulatory system. The AISI-H strain can achieve recovery of dynamic tumor-specific bacterial virulence through a novel HTRA-RCSA axis-based and quorum-sensing synthetic gene circuit-mediated increase in extracellular polysaccharide content. These strains act "off" in normal organs to avoid unwanted immune activation and "on" in tumors for precise tumor suppression in mice. The AISI-H strain shows significant tumor inhibition and potent activation of anticancer immunity in a melanoma mouse model. The AISI-H strain exhibits excellent biocompatibility. This bacterial regulation strategy expands the applications of microbe-based antitumor therapeutics.

Breast lesions segmentation from ultrasound images using DeepLabV3 + model with channel and spatial attention mechanism

Segmenting breast tumors in ultrasound images is pivotal for computer-aided diagnosis (CAD) systems aimed at detecting breast cancer. This research contributes directly to the Sustainable Development Goal (SDG), good health and well-being by leveraging innovative deep learning technique to boost the accuracy of breast tumor segmentation. Achieving accurate segmentation is crucial for precise tumor size, shape, and location determination, which in turn facilitates subsequent tumor quantification and classification. However, this task presents challenges, particularly when dealing with small tumors. The complexity arises from factors such as speckle noise, variations in tumor shapes and sizes across patients, and the presence of tumor-like regions in the images. Although deep learning-based methods have shown remarkable success in biomedical image analysis, the current state-of-the-art approaches still struggle to achieve satisfactory performance in segmenting small breast tumors. In this paper, BUS (Breast Ultrasound) dataset was initially applied on state of-the art DeepLabV3 + architecture for the segmentation of breast lesions in ultrasound images. After that to focus on more informative features, a modified DeepLabV3 + has been proposed by integrating a CBAM module in encoder as well as decoder. At the end, comparative analysis of state-of-art DeepLabV3 + model and proposed modified DeepLabV3 + has also been presented in terms of performance metrics like Dice Coefficient, Intersection over Union, Precision, Recall and Specificity. The proposed model demonstrates outstanding performance across different evaluation metrics achieving precision, recall, specificity, dice coefficient and Intersection over Union (IoU) values of 0.974, 0.933, 0.997, 0.951, and 0.933. respectively.

Biomimetic Gd-Metal-Organic Framework Radiosensitizer for Near-Infrared Fluorescence Imaging-Guided Radiotherapy toward Nasopharyngeal Carcinoma.

Radiotherapy (RT) is recognized as a primary treatment modality for Nasopharyngeal carcinoma (NPC). However, enhancing RT's targeting accuracy and selectivity remains a significant challenge. In this study, we present an innovative radiosensitizer, Gd-metal-organic framework (MOF)-based nanocarrier coated with indocyanine green (ICG) and red blood cell membrane (RBCM), designed to bypass immune clearance and achieve prolonged circulation within the bloodstream. This design significantly enhances tumor localization and systemic circulation, as evidenced by in vivo analyses. The strategic accumulation of the Gd-MOF-ICG nanocarrier at the tumor site facilitates precise tumor localization and sensitization to RT, leveraging the RBCM camouflage to enhance the tumor uptake potential. Our comprehensive study introduces a potent approach for optimizing RT in NPC treatment through this advanced theranostic nanoplatform, which combines material science with biomedical engineering to augment the effectiveness of RT and underscores the significance of precision in cancer therapy. This strategy offers a promising avenue for clinical application and further research in targeted cancer treatments.

Automated brain tumor diagnostics: Empowering neuro-oncology with deep learning-based MRI image analysis.

Brain tumors, characterized by the uncontrolled growth of abnormal cells, pose a significant threat to human health. Early detection is crucial for successful treatment and improved patient outcomes. Magnetic Resonance Imaging (MRI) is the primary diagnostic tool for brain tumors, providing detailed visualizations of the brain's intricate structures. However, the complexity and variability of tumor shapes and locations often challenge physicians in achieving accurate tumor segmentation on MRI images. Precise tumor segmentation is essential for effective treatment planning and prognosis. To address this challenge, we propose a novel hybrid deep learning technique, Convolutional Neural Network and ResNeXt101 (ConvNet-ResNeXt101), for automated tumor segmentation and classification. Our approach commences with data acquisition from the BRATS 2020 dataset, a benchmark collection of MRI images with corresponding tumor segmentations. Next, we employ batch normalization to smooth and enhance the collected data, followed by feature extraction using the AlexNet model. This involves extracting features based on tumor shape, position, shape, and surface characteristics. To select the most informative features for effective segmentation, we utilize an advanced meta-heuristics algorithm called Advanced Whale Optimization (AWO). AWO mimics the hunting behavior of humpback whales to iteratively search for the optimal feature subset. With the selected features, we perform image segmentation using the ConvNet-ResNeXt101 model. This deep learning architecture combines the strengths of ConvNet and ResNeXt101, a type of ConvNet with aggregated residual connections. Finally, we apply the same ConvNet-ResNeXt101 model for tumor classification, categorizing the segmented tumor into distinct types. Our experiments demonstrate the superior performance of our proposed ConvNet-ResNeXt101 model compared to existing approaches, achieving an accuracy of 99.27% for the tumor core class with a minimum learning elapsed time of 0.53 s.

The relationship between lobar distribution and primary lung cancer histological types

Background: Lung cancer is the primary cause of cancer-related deaths globally. Accurate tumor location and subtype characterization are critical for optimizing treatment and predicting outcomes. This study aims to identify the relationship between lobar distribution and primary lung cancer histological types and describe the most recent patterns at the country-level origin. Methods: This cross-sectional study was conducted at a major tertiary care hospital in Bangladesh, including 165 patients recruited using purposive non-probability sampling. Data were collected using a semi-structured case record form and analyzed using SPSS version 28.0. Results: Tumors were most commonly found in the central area (58.2%). Squamous cell carcinoma was the most prevalent type (46.1%), followed by adenocarcinoma (38.8%), small cell carcinoma (13.9%), and large cell carcinoma (1.2%). The incidence of squamous cell carcinoma was significantly higher in the upper lobe of the right lung (55.9%) compared to other forms, particularly in the central region of the right lung (P < 0.001). Squamous cell carcinoma was more common in the lower part of the left lung (42.9%), while adenocarcinoma was exclusively observed in the lingular portion of the left lung (100%). The major bronchus of the left lung primarily exhibited squamous cell carcinoma (60%), but this finding was not statistically significant (p=0.899). Conclusion: The study highlights the significant association between lobar distribution and histological types of primary lung cancer, emphasizing the need for precise tumor characterization for better treatment strategies and prognostic evaluation.

NIR laser-activated phthalocyanine loaded lipid nanoparticles targeting M2 macrophage for improved photoacoustic imaging-guided photothermal therapy

The development of novel phototheranostic agents with significant potential in bioimaging-guided therapy is highly desirable for precise tumor therapy. Herein, NIR laser-activated ruthenium phthalocyanine (PcRu) loaded sub-30 nm targeting lipid nanoparticles (α-PcRu-NPs) were fabricated for photoacoustic imaging (PAI)-guided photothermal therapy (PTT). Due to the formation of J-type aggregation of PcRu in the core of the nanostructure, the α-PcRu-NPs exhibited high stability, efficient NIR absorption, reduced singlet oxygen generation, high photothermal activity, and intense photoacoustic signal. With the M2 macrophage target peptide (M2pep) modification and small size of α-PcRu-NPs, in vivo evaluations reveal that α-PcRu-NPs can specifically target and deeply penetrate the tumor foci. Under a high contrast PAI guidance with α-PcRu-NPs (744 nm, 0.35 μW), it also realizes superior photothermal therapy (PTT) for breast cancer under 670 nm laser irradiation (0.5 W/cm2). The prominent therapeutic efficacy of α-PcRu-NP-based PTT not only directly kills tumor cells, but also enhances the immune response by promoting dendritic cell maturation and increasing cytotoxic T cell infiltration. Thus, this work broadens the applications of phthalocyanine derivatives as phototheranostics in the PAI-guided PTT field.

In-situ activated arsenic-molybdenum dual-prodrug nanocomplexes for glutathione-depletion enhanced photothermal/chemotherapy against triple-negative breast cancer

Arsenic trioxide, a traditional chemotherapeutic agent, has limitations in clinical application due to its nonspecific delivery and severe systemic toxicity. To address this challenge, we have capitalized on the promising potential of molybdenum-based materials for tumor diagnosis and treatment. We developed a novel multifunctional arsenic-molybdenum dual-prodrug nanocomposite delivery system (AsMo-NCM) by integrating arsenic compounds with molybdenum compounds. This system leverages elevated glutathione (GSH) levels within tumors for specific activation. Within the tumor microenvironment, the system converts Mo(VI) to the more active Mo(V), enabling combined photothermal therapy and photoacoustic imaging for comprehensive diagnosis and treatment. Furthermore, low-toxicity arsenic(V) is converted into highly toxic trivalent arsenic within the high-GSH tumor microenvironment, thereby enhancing tumor cell apoptosis, reducing toxicity to normal tissues, and synergistically improving treatment outcomes. In addition, AsMo-NCM can be biodegraded and rapidly excreted, reducing concerns about their long-term presence and possible toxic effects. The combination of these approaches maximizes GSH depletion, thereby enhancing the synergistic photothermal/chemotherapy effect of AsMo-NCM for precise and effective tumor treatment, thus offering promising prospects for cancer therapy.

From Data Mining of Public Sources to Clinical Decision Support in Personalized Medicine.

Personalized medicine enables precise tumor treatment for a patient's molecular genetic profile. To devise optimal targeted treatment plans for patients in a molecular tumor board, physicians must consider alterations on gene- and proteins levels but also cancer cell phenotypes. Machine learning can uncover buried patterns, extract pivotal information, and unveil corresponding insights from available data. Publicly available datasets provide the amounts of data necessary. This work outlines the efficacy of various machine learning algorithms which could eventually serve as clinical decision support in a precision oncology setting. Leveraging algorithms including Random Forest, Decision tree, XGBoost, Logistic regression, Gaussian Naive Bayes, k nearest neighbor, and AdaBoost, we conducted two experiments for the breast invasive carcinoma dataset. Incorporated data includes patient-, molecular- and treatment data. The aim of the investigation was to predict medication treatment or type of treatment based on genetic profile. After preprocessing and application of ML algorithms, the first results were promising. Multiple factors challenge application in clinical care settings without carefully considering the limitations.

Gold Nano Frameworks with Mesopores for Synergistic Immune-Thermal Therapy in Hepatic Carcinoma: A Paradigm Shift in Immune Checkpoint Blockade.

Immune checkpoint blockade (ICB) therapy, while showing promise in various cancers, exhibits limited effectiveness in hepatic carcinoma due to the tumor's immunosuppressive microenvironment (TME) and challenges associated with immune cell infiltration. Efforts to transform the "cold" TME into an "inflamed" state, notably through chemo-immunotherapy, have sparked interest due to their potential to induce immunogenic cell death and augment the infiltration of cytotoxic T lymphocytes (CTLs). Nonetheless, the efficacy of chemo-immunotherapy is often compromised by suboptimal pharmacokinetics, poor tumor accumulation, and off-target toxicity. Herein, in response, we introduce an innovative, milder thermal therapeutic approach leveraging gold nano frameworks with mesopores for the targeted delivery of the immunostimulant imiquimod and NIR-II photothermal therapy. This strategy employs targeted molecule modifications to ensure precise tumor targeting, guided by photoacoustic imaging. Subsequent to mild thermal treatment, there is a release of immunogenic proteins (CRT and HSP90), enhancing tumor immunogenicity. Assisted by imiquimod, substantial CTL infiltration occurs, accompanied by pro-inflammatory factor release (TNF-α, IL-6), transforming M2 macrophages into the M1 phenotype. Ultimately, the proposed strategy combines PD-L1/PD-1 blockade, imiquimod and mild thermal treatment to synergistically enhance tumor immunogenicity, remodel the TME, and restrain hepatic carcinoma, making strides in ICB synergistic immune-thermal therapy.

Au@CuS Nanoshells for Surface-Enhanced Raman Scattering Image-Guided Tumor Photothermal Therapy with Accelerated Hepatobiliary Excretion.

Gold-based nanoparticles for surface-enhanced Raman scattering (SERS) imaging show great potential for precise tumor detection and photothermal therapy (PTT). However, the metabolizability of gold nanoparticles (Au NPs) raises big concerns. Herein, we designed a core-shelled nanostructure of copper sulfide (CuS)-coated Au NPs with surface pegylation (PEG-Au@CuS NSs). The excreted Au in the gallbladders at 1 h and 4 h in mice injected with PEG-Au@CuS NSs was 8.2- and 19.1-fold of that with the pegylated Au NPs (PEG-AuNPs) of the same Au particle size, respectively. By loading the Raman reporter 3,3'-Diethylthiatricarbocyanine iodide (DTTC) in the core-shell junction of PEG-Au@CuS NSs, the PEG-Au-DTTC@CuS NSs exhibited the Raman signal-to-noise (S/N) ratio of 4.01 after 24 h of intravenous (IV) injection in the mice bearing an orthotopic CT26-Luc colon tumor. By contrast, the DTTC-coated PEG-AuNPs (PEG-Au-DTTC NPs) achieved an S/N ratio of 2.71. Moreover, PEG-Au-DTTC@CuS NSs exhibited an increased photothermal conversion effect compared with PEG-Au-DTTC NPs excited with an 808-nm laser. PEG-Au-DTTC@CuS NSs enabled intraoperative SERS image-guided photothermal therapy for a complete cure of the colon tumor-bearing mice. Our data demonstrated that the PEG-Au-DTTC@CuS NSs are promising intraoperative Raman image-guided theranostic nanoplatform with enhanced hepatobiliary excretion.

Accurate MRI-Based Brain Tumor Diagnosis: Integrating Segmentation and Deep Learning Approaches

Magnetic Resonance Imaging (MRI) is vital in diagnosing brain tumours, offering crucial insights into tumour morphology and precise localisation. Despite its pivotal role, accurately classifying brain tumours from MRI scans is inherently complex due to their heterogeneous characteristics. This study presents a novel integration of advanced segmentation methods with deep learning ensemble algorithms to enhance the classification accuracy of MRI-based brain tumour diagnosis. We conduct a thorough review of both traditional segmentation approaches and contemporary advancements in region-based and machine learning-driven segmentation techniques. This paper explores the utility of deep learning ensemble algorithms, capitalising on the diversity of model architectures to augment tumour classification accuracy and robustness. Through the synergistic amalgamation of sophisticated segmentation techniques and ensemble learning strategies, this research addresses the shortcomings of traditional methodologies, thereby facilitating more precise and efficient brain tumour classification.

A Green Lantern for the Surgeon: A Review on the Use of Indocyanine Green (ICG) in Minimally Invasive Surgery.

Indocyanine green (ICG) fluorescence imaging has revolutionized surgical practice across various medical and surgical specialties. This article reviews the clinical applications of ICG in abdominal, urological, thoracic, and gynecological surgery. ICG fluorescence imaging has been widely adopted in general surgery for various applications, including perfusion assessment, intraoperative visualization of the ureter, and tumor localization. It is particularly valuable in evaluating anastomotic leaks and aiding in precise tumor resection during minimally invasive surgeries. Studies have shown mixed results on its effectiveness in reducing anastomotic leak rates, highlighting the need for further research. In thoracic surgery, ICG facilitates the identification and resection of pulmonary bullae, as well as the precise localization of pulmonary nodules during video-assisted surgery. In urology, ICG aids in localizing renal tumors and guiding selective arterial occlusion during partial nephrectomy. Its role in identifying the lymphatic pathway in prostate cancer and sentinel lymph node biopsy in gynecological cancer is also discussed. Despite its benefits, the use of ICG fluorescence faces challenges such as limited tissue penetration, the potential for false results, a lack of standardized protocols, and high equipment costs. Nonetheless, it remains a powerful tool that could improve surgical outcomes.

Enhancing liver tumor segmentation with UNet-ResNet: Leveraging ResNet’s power

BACKGROUND: Liver cancer poses a significant health challenge due to its high incidence rates and complexities in detection and treatment. Accurate segmentation of liver tumors using medical imaging plays a crucial role in early diagnosis and treatment planning. OBJECTIVE: This study proposes a novel approach combining U-Net and ResNet architectures with the Adam optimizer and sigmoid activation function. The method leverages ResNet’s deep residual learning to address training issues in deep neural networks. At the same time, U-Net’s structure facilitates capturing local and global contextual information essential for precise tumor characterization. The model aims to enhance segmentation accuracy by effectively capturing intricate tumor features and contextual details by integrating these architectures. The Adam optimizer expedites model convergence by dynamically adjusting the learning rate based on gradient statistics during training. METHODS: To validate the effectiveness of the proposed approach, segmentation experiments are conducted on a diverse dataset comprising 130 CT scans of liver cancers. Furthermore, a state-of-the-art fusion strategy is introduced, combining the robust feature learning capabilities of the UNet-ResNet classifier with Snake-based Level Set Segmentation. RESULTS: Experimental results demonstrate impressive performance metrics, including an accuracy of 0.98 and a minimal loss of 0.10, underscoring the efficacy of the proposed methodology in liver cancer segmentation. CONCLUSION: This fusion approach effectively delineates complex and diffuse tumor shapes, significantly reducing errors.

A multifunctional mesoporous silica drug delivery nanosystem that ameliorates tumor hypoxia and increases radiotherapy efficacy

Radiotherapy (RT) is a widely used treatment with strong therapeutic effects, but overcoming challenges related to hypoxia-induced tumor resistance and ineffective antitumor immune responses is crucial for optimal outcomes. In this study, we developed a versatile nanosystem using mesoporous silica nanoparticles (MSNs), R837, and a small quantity of manganese peroxide (Mn/ZnO2). The synthesized MSN@R837-Mn/ZnO2 nanoparticles exhibited precise tumor targeting and accumulation, controlled drug release under acidic conditions, and increased sensitivity in magnetic resonance imaging. These attributes collectively augmented the therapeutic efficacy of RT by alleviating hypoxia and immunosuppression. Tumor cells treated with RT combined with these nanoparticles displayed reduced oxidative stress, alleviated hypoxia, and normalized blood vessel formation. Notably, all mice in the RT + PD-1 + MSN@R837-Mn/ZnO2 group achieved complete tumor regression with extended survival. Safety assessments confirmed the absence of MSN@R837-Mn/ZnO2 toxicity, highlighting its potential as a promising approach with dual functionality for the diagnostic imaging and treatment of cancer.

Self-reporting photodynamic nanobody conjugate for precise and sustainable large-volume tumor treatment

Nanobodies (Nbs), the smallest antigen-binding fragments with high stability and affinity derived from the variable domain of naturally occurring heavy-chain-only antibodies in camelids, have been shown as an efficient way to improve the specificity to tumors for photodynamic therapy (PDT). Nonetheless, the rapid clearance of Nbs in vivo restricts the accumulation and retention of the photosensitizer at the tumor site causing insufficient therapeutic outcome, especially in large-volume tumors. Herein, we develop photodynamic conjugates, MNB-Pyra Nbs, through site-specific conjugation between 7D12 Nbs and type I photosensitizer MNB-Pyra (morpholine-modified nile blue structure connected to pyrazolinone) in a 1:2 ratio. The photosensitizers with long-term retention can be released at the tumor site by reactive oxygen species cleavage after illumination, accompanied with fluorescence recovery for self-reporting the occurrence of PDT. Ultimately, a single dose of MNB-Pyra Nbs demonstrate highly effective tumor suppression with high biosafety in the large-volume tumor models after three rounds of PDT. This nanobody conjugate provides a paradigm for the design of precise long-time retention photosensitizers and is expected to promote the development of PDT.

Precise liver tumor ablation: the clinical potential of US-US overlay fusion guidance.

Image-guided thermal ablation is a minimally invasive option for patients with early-stage hepatocellular carcinoma (HCC). However, the risk of local recurrence remains substantial because ultrasound (US) artifacts have a negative impact on the assessment of ablative margins during and immediately after ablation. Precise, real-time assessment of the ablation zone is key to reducing the risk of local tumor progression. With the advent of US image fusion technology, ablative margins can now be assessed three-dimensionally with greater accuracy. Therefore, US-US overlay fusion guidance has the potential to improve the local controllability of ablation in patients with HCC. This review discusses the US-US fusion guidance technique and its current clinical applications for hepatic interventions, with descriptions of its concept, methodology, and efficacy.

Application of artificial intelligence in the diagnosis and treatment of urinary tumors.

Diagnosis and treatment of urological tumors, relying on auxiliary data such as medical imaging, while incorporating individual patient characteristics into treatment selection, has long been a key challenge in clinical medicine. Traditionally, clinicians used extensive experience for decision-making, but recent artificial intelligence (AI) advancements offer new solutions. Machine learning (ML) and deep learning (DL), notably convolutional neural networks (CNNs) in medical image recognition, enable precise tumor diagnosis and treatment. These technologies analyze complex medical image patterns, improving accuracy and efficiency. AI systems, by learning from vast datasets, reveal hidden features, offering reliable diagnostics and personalized treatment plans. Early detection is crucial for tumors like renal cell carcinoma (RCC), bladder cancer (BC), and Prostate Cancer (PCa). AI, coupled with data analysis, improves early detection and reduces misdiagnosis rates, enhancing treatment precision. AI's application in urological tumors is a research focus, promising a vital role in urological surgery with improved patient outcomes. This paper examines ML, DL in urological tumors, and AI's role in clinical decisions, providing insights for future AI applications in urological surgery.

Phase Ib trial of durvalumab plus tremelimumab in combination with particle therapy in patients with advanced hepatocellular carcinoma with macrovascular invasion: DEPARTURE trial.

34 Background: The HIMALAYA trial established the combination of durvalumab and tremelimumab (STRIDE) as first-line treatment for advanced hepatocellular carcinoma (HCC). Carbon ion radiotherapy (C-ion RT) allows precise tumor targeting while sparing nearby normal tissue. Our strategy used C-ion RT to target a key tumor with macrovascular invasion (MVI) for disease management and potential MVI control, complemented by STRIDE. Recognising that C-ion RT stimulates immune responses, we posited that merging C-ion RT with STRIDE might enhance therapeutic effects. We present updated data of additional follow-up. Methods: This is a Phase Ib, multicenter study evaluating durvalumab with particle beam radiotherapy in HCC patients with MVI (Cohort A) and STRIDE (Cohort B). Both cohorts began with patients resistant to standard treatments. After safety confirmation, the study expanded to include therapy-naïve patients, totaling 15. C-ion RT begins after day 8 of the first cycle, following the initial drug dose. The relative biological effectiveness weighted dose is set at 60 Gy in four fractions over one week, targeting one representative intrahepatic nodule with MVI. The primary endpoint is adverse event rates, including dose limiting toxicity (DLT), with secondary endpoints on progression free survival (PFS) and overall survival (OS) (jRCT2031210046). This analysis of OS was conducted from the additional follow-up data from the primary analysis. Results: From July 2021 to January 2023, 15 advanced HCC patients were enrolled in our study (Cohort A: 3, Cohort B: 12). Four in Cohort B were systemic therapy-naïve. All cases confirmed MVI radiologically; with tumor invasion observed in the main portal vein for 4 patients and in the inferior vena cava for one. DLT evaluations in 3 from each cohort showed no incidents. While Cohort A had no adverse events, 4 patients in Cohort B (26.7%) experienced serious treatment-related adverse events. The median PFS was a significant 4.67 months (95%CI, 1.35–6.64). At the clinical cut-off date of March 31, 2024, 10 OS events were observed. The median OS was 10.4 months (95% CI, 5.6–15.2), and the 6- and 12-month OS rates were 66.7% and 46.7%, respectively. Conclusions: The safety of STRIDE in combination with particle therapy has been confirmed in advanced HCC with MVI. This combination, especially the irradiation of tumors with MVI using C-ion RT, holds promise in managing prognostic determinant tumors and potentially enhancing OS. Clinical trial information: jRCT2031210046 .